About Tony Sarris – N2Semantics

I am the founder and principal of N2Semantics. I provide consulting services to companies developing and applying semantic technologies, including artificial intelligence and cognitive computing.
At N2Semantics we believe the future of apps is to make them smarter -- more aware of the world in which they operate to serve the needs and interests of their users.
Our mission is to foster the development of semantic technologies and to help companies determine the best approaches and technologies for adding semantic knowledge or meaning to their applications.

This blog post isn’t about some superhero or secret agent code-named Action. It’s about enabling intelligent software agents to take action. As I’ve been writing periodically about intelligent software agents or virtual personal assistants, I’ve not shied away from saying there are significant challenges to making them commonplace in our everyday lives. But that doesn’t mean we shouldn’t be starting to build intelligent agents today.

One challenge is providing software agents knowledge about the domains in which they are intended to operate, in other words making software agents ‘intelligent’. In my last post, “Teaching a Martian to Make a Martini”, I tried to provide a sense of the scale of the challenge involved in making software agents intelligent, and pointed to ways to get started using semantic networks (whether constructed, induced, blended or generated). There are at least two other significant challenges: describing or modeling the actions to be performed and specifying the triggers/conditions, inputs, controls and expected outputs from those actions. These are of course intertwined with having underlying knowledge of the domain, as these challenges involve putting domain knowledge in a form and context such that the software agent can make use of it to perform one or more tasks for their human ‘employer’.

Here’s the first secret to producing agents capable of action: create the models of the actions or processes (at least in rough form) through ‘simply’ capturing instructions via natural language processing, perhaps combined with haptic signals (such as click patterns) from the manual conduct of the tasks on devices such as smart phones. Thinking of the example from my previous post, this is the equivalent of you telling the Martian the steps to be executed to make a martini or going through the steps yourself with the Martian observing the process. In either case, this process model includes the tasks to be performed and the associated work flow (including critical path and optional or alternative steps), as well as triggers and conditions (such as prerequisites, dependencies, etc.) for the execution of the steps and the inputs, outputs and controls for those steps. Keywords extracted from the instructions can serve as the basis for building out a richer, underlying contextual model in the form of a semantic network or ontology. But there is more work to be done. A state like the existence of a certain input or the occurrence of an event such as the completion of a prior task can serve as a trigger, and the output of one process can be an input to one or more others. There can be loops, and since there can be unexpected situations, there can be other actions to take when things don’t go according to plan. Process modeling can get quite complex. Writing code can be a form of process modeling, where the code is a model or a model can be created in a form that is executable by some state machine. But we don’t want to have to do either of those in this case. The goal should be to naturally evolve these models, not to require they be developed in all their detail before they get used by an agent for the first time. And more general models that can be specialized as they get applied are the best case scenario.

I know a fair bit about complex process models. I encoded a model of the product definition (i.e., product design) process for aircraft (as developed by a team at Northrop Corporation — with a shout out here to @AlexTu) into a form executable by an artificial intelligence/expert system. My objective at the time was to test an approach to modeling of a process ontology and an associated AI technology built around it. The objective of Northrop was to be able to have a software system ‘understand’ things like the relationships among the process steps, related artifacts (e.g., input and outputs) and conditionals and to be able to optimize the process through eliminating unnecessary steps and introducing more parallelism. In other words, a major goal of the project was to enable more of what was called at the time ‘concurrent engineering’, both to shorten the time needed for product definition and to catch problems with the design of the product as early in the process as possible (since the later such problems are caught, the more they cost to correct — with the worst case being of course that a problem isn’t discovered until the aircraft has been built and is deployed and in use for its mission in the field). The project was pretty darned impressive, and the technology worked well as an ‘assistant’ to product engineers looking to improve their processes.

Many of the tasks we deal with on a regular basis in our everyday lives aren’t as complex or specialized as the product definition process for an aircraft. But even relatively simple processes can be tedious to encode if every detail has to be explicitly modeled. Here is where another secret comes in: rather than model detailed conditionals for things like triggers, why not use statistical data about the presence of certain indicative concepts in input and output data associated with the process, along with refinements to the model based on user feedback? Clearly this approach makes the most sense for non-critical processes. You don’t want to try it for brain surgery (at least not if you are one of the first patients). But virtual personal assistants and other agents aren’t intended to do jobs for us entirely on their own, so much as to help us do our job (at least at first). So if we have some patience up-front and are willing to work with ‘good enough’, I think we could see a lot more examples of such software agents. If we have expectations that the agents know everything and are right close to 100% of the time, we’ll see a lot fewer. It’s that simple, I think.

So let’s get started building and using some ‘good enough’ assistants. If other people want to wait until they’re ‘perfected’, they can join the party later, maybe after The Singularity has occurred. I think it is time to start now. And I’m convinced we’ll get farther faster if we do start now, rather than waiting until years from now to begin building such technologies en masse. Let’s refocus some of our collective efforts from yet-another social networking app or more cute cat videos onto more intelligent agents. Then intelligent, actionable software agents won’t be so secret anymore – in fact, they’ll be everywhere. And you’ll have more free time to spend with your cat.